Overview

Dataset statistics

Number of variables24
Number of observations260
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.9 KiB
Average record size in memory192.5 B

Variable types

Categorical2
Text2
Numeric20

Alerts

Calcium (% Daily Value) is highly overall correlated with Calories and 5 other fieldsHigh correlation
Calories is highly overall correlated with Calcium (% Daily Value) and 15 other fieldsHigh correlation
Calories from Fat is highly overall correlated with Calories and 12 other fieldsHigh correlation
Carbohydrates is highly overall correlated with Calcium (% Daily Value) and 3 other fieldsHigh correlation
Carbohydrates (% Daily Value) is highly overall correlated with Calcium (% Daily Value) and 3 other fieldsHigh correlation
Cholesterol is highly overall correlated with Calories and 13 other fieldsHigh correlation
Cholesterol (% Daily Value) is highly overall correlated with Calories and 13 other fieldsHigh correlation
Dietary Fiber is highly overall correlated with Calories and 9 other fieldsHigh correlation
Dietary Fiber (% Daily Value) is highly overall correlated with Calories and 11 other fieldsHigh correlation
Iron (% Daily Value) is highly overall correlated with Calories and 14 other fieldsHigh correlation
Protein is highly overall correlated with Calcium (% Daily Value) and 13 other fieldsHigh correlation
Saturated Fat is highly overall correlated with Calories and 10 other fieldsHigh correlation
Saturated Fat (% Daily Value) is highly overall correlated with Calories and 10 other fieldsHigh correlation
Sodium is highly overall correlated with Calories and 12 other fieldsHigh correlation
Sodium (% Daily Value) is highly overall correlated with Calories and 12 other fieldsHigh correlation
Sugars is highly overall correlated with Calcium (% Daily Value) and 2 other fieldsHigh correlation
Total Fat is highly overall correlated with Calories and 12 other fieldsHigh correlation
Total Fat (% Daily Value) is highly overall correlated with Calories and 12 other fieldsHigh correlation
Trans Fat is highly overall correlated with Cholesterol and 2 other fieldsHigh correlation
Vitamin A (% Daily Value) is highly overall correlated with Calcium (% Daily Value) and 2 other fieldsHigh correlation
Vitamin C (% Daily Value) is highly overall correlated with Dietary Fiber and 2 other fieldsHigh correlation
Trans Fat is highly imbalanced (53.6%)Imbalance
Item has unique valuesUnique
Calories has 16 (6.2%) zerosZeros
Calories from Fat has 54 (20.8%) zerosZeros
Total Fat has 49 (18.8%) zerosZeros
Total Fat (% Daily Value) has 49 (18.8%) zerosZeros
Saturated Fat has 60 (23.1%) zerosZeros
Saturated Fat (% Daily Value) has 60 (23.1%) zerosZeros
Cholesterol has 44 (16.9%) zerosZeros
Cholesterol (% Daily Value) has 44 (16.9%) zerosZeros
Sodium has 9 (3.5%) zerosZeros
Sodium (% Daily Value) has 20 (7.7%) zerosZeros
Carbohydrates has 16 (6.2%) zerosZeros
Carbohydrates (% Daily Value) has 16 (6.2%) zerosZeros
Dietary Fiber has 69 (26.5%) zerosZeros
Dietary Fiber (% Daily Value) has 69 (26.5%) zerosZeros
Sugars has 25 (9.6%) zerosZeros
Protein has 27 (10.4%) zerosZeros
Vitamin A (% Daily Value) has 62 (23.8%) zerosZeros
Vitamin C (% Daily Value) has 162 (62.3%) zerosZeros
Calcium (% Daily Value) has 36 (13.8%) zerosZeros
Iron (% Daily Value) has 82 (31.5%) zerosZeros

Reproduction

Analysis started2024-04-26 20:21:16.952907
Analysis finished2024-04-26 20:23:22.123815
Duration2 minutes and 5.17 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Category
Categorical

Distinct9
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Coffee & Tea
95 
Breakfast
42 
Smoothies & Shakes
28 
Chicken & Fish
27 
Beverages
27 
Other values (4)
41 

Length

Max length18
Median length14
Mean length11.853846
Min length6

Characters and Unicode

Total characters3082
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreakfast
2nd rowBreakfast
3rd rowBreakfast
4th rowBreakfast
5th rowBreakfast

Common Values

ValueCountFrequency (%)
Coffee & Tea 95
36.5%
Breakfast 42
16.2%
Smoothies & Shakes 28
 
10.8%
Chicken & Fish 27
 
10.4%
Beverages 27
 
10.4%
Beef & Pork 15
 
5.8%
Snacks & Sides 13
 
5.0%
Desserts 7
 
2.7%
Salads 6
 
2.3%

Length

2024-04-26T23:23:22.399816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T23:23:22.802819image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
178
28.9%
coffee 95
15.4%
tea 95
15.4%
breakfast 42
 
6.8%
smoothies 28
 
4.5%
shakes 28
 
4.5%
chicken 27
 
4.4%
fish 27
 
4.4%
beverages 27
 
4.4%
beef 15
 
2.4%
Other values (5) 54
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e 548
17.8%
356
11.6%
a 259
 
8.4%
f 247
 
8.0%
s 205
 
6.7%
& 178
 
5.8%
o 166
 
5.4%
k 125
 
4.1%
C 122
 
4.0%
h 110
 
3.6%
Other values (16) 766
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 548
17.8%
356
11.6%
a 259
 
8.4%
f 247
 
8.0%
s 205
 
6.7%
& 178
 
5.8%
o 166
 
5.4%
k 125
 
4.1%
C 122
 
4.0%
h 110
 
3.6%
Other values (16) 766
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 548
17.8%
356
11.6%
a 259
 
8.4%
f 247
 
8.0%
s 205
 
6.7%
& 178
 
5.8%
o 166
 
5.4%
k 125
 
4.1%
C 122
 
4.0%
h 110
 
3.6%
Other values (16) 766
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 548
17.8%
356
11.6%
a 259
 
8.4%
f 247
 
8.0%
s 205
 
6.7%
& 178
 
5.8%
o 166
 
5.4%
k 125
 
4.1%
C 122
 
4.0%
h 110
 
3.6%
Other values (16) 766
24.9%

Item
Text

UNIQUE 

Distinct260
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:23.570820image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length61
Median length46
Mean length28.984615
Min length5

Characters and Unicode

Total characters7536
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)100.0%

Sample

1st rowEgg McMuffin
2nd rowEgg White Delight
3rd rowSausage McMuffin
4th rowSausage McMuffin with Egg
5th rowSausage McMuffin with Egg Whites
ValueCountFrequency (%)
large 55
 
4.9%
with 51
 
4.6%
medium 48
 
4.3%
small 46
 
4.1%
chicken 44
 
3.9%
biscuit 34
 
3.1%
iced 31
 
2.8%
egg 30
 
2.7%
nonfat 30
 
2.7%
latte 30
 
2.7%
Other values (141) 715
64.2%
2024-04-26T23:23:24.786815image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
854
 
11.3%
e 700
 
9.3%
a 589
 
7.8%
i 433
 
5.7%
r 349
 
4.6%
t 336
 
4.5%
l 315
 
4.2%
c 295
 
3.9%
h 267
 
3.5%
u 248
 
3.3%
Other values (52) 3150
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
854
 
11.3%
e 700
 
9.3%
a 589
 
7.8%
i 433
 
5.7%
r 349
 
4.6%
t 336
 
4.5%
l 315
 
4.2%
c 295
 
3.9%
h 267
 
3.5%
u 248
 
3.3%
Other values (52) 3150
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
854
 
11.3%
e 700
 
9.3%
a 589
 
7.8%
i 433
 
5.7%
r 349
 
4.6%
t 336
 
4.5%
l 315
 
4.2%
c 295
 
3.9%
h 267
 
3.5%
u 248
 
3.3%
Other values (52) 3150
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
854
 
11.3%
e 700
 
9.3%
a 589
 
7.8%
i 433
 
5.7%
r 349
 
4.6%
t 336
 
4.5%
l 315
 
4.2%
c 295
 
3.9%
h 267
 
3.5%
u 248
 
3.3%
Other values (52) 3150
41.8%
Distinct107
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:25.663817image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length17
Median length12
Mean length12.911538
Min length10

Characters and Unicode

Total characters3357
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)31.9%

Sample

1st row4.8 oz (136 g)
2nd row4.8 oz (135 g)
3rd row3.9 oz (111 g)
4th row5.7 oz (161 g)
5th row5.7 oz (161 g)
ValueCountFrequency (%)
oz 256
24.6%
fl 140
13.5%
cup 138
13.3%
g 118
11.3%
16 45
 
4.3%
12 38
 
3.7%
22 20
 
1.9%
20 16
 
1.5%
30 7
 
0.7%
21 7
 
0.7%
Other values (174) 255
24.5%
2024-04-26T23:23:27.228819image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
780
23.2%
o 262
 
7.8%
z 256
 
7.6%
1 235
 
7.0%
2 178
 
5.3%
l 143
 
4.3%
c 142
 
4.2%
f 140
 
4.2%
p 138
 
4.1%
u 138
 
4.1%
Other values (20) 945
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
780
23.2%
o 262
 
7.8%
z 256
 
7.6%
1 235
 
7.0%
2 178
 
5.3%
l 143
 
4.3%
c 142
 
4.2%
f 140
 
4.2%
p 138
 
4.1%
u 138
 
4.1%
Other values (20) 945
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
780
23.2%
o 262
 
7.8%
z 256
 
7.6%
1 235
 
7.0%
2 178
 
5.3%
l 143
 
4.3%
c 142
 
4.2%
f 140
 
4.2%
p 138
 
4.1%
u 138
 
4.1%
Other values (20) 945
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
780
23.2%
o 262
 
7.8%
z 256
 
7.6%
1 235
 
7.0%
2 178
 
5.3%
l 143
 
4.3%
c 142
 
4.2%
f 140
 
4.2%
p 138
 
4.1%
u 138
 
4.1%
Other values (20) 945
28.2%

Calories
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.26923
Minimum0
Maximum1880
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:27.740817image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1210
median340
Q3500
95-th percentile762
Maximum1880
Range1880
Interquartile range (IQR)290

Descriptive statistics

Standard deviation240.26989
Coefficient of variation (CV)0.65242998
Kurtosis5.7787963
Mean368.26923
Median Absolute Deviation (MAD)140
Skewness1.4524982
Sum95750
Variance57729.618
MonotonicityNot monotonic
2024-04-26T23:23:28.196818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
6.2%
340 10
 
3.8%
430 10
 
3.8%
280 9
 
3.5%
250 8
 
3.1%
260 7
 
2.7%
330 7
 
2.7%
140 7
 
2.7%
270 6
 
2.3%
450 6
 
2.3%
Other values (68) 174
66.9%
ValueCountFrequency (%)
0 16
6.2%
15 1
 
0.4%
20 1
 
0.4%
45 1
 
0.4%
80 2
 
0.8%
100 5
 
1.9%
110 2
 
0.8%
120 2
 
0.8%
130 4
 
1.5%
140 7
2.7%
ValueCountFrequency (%)
1880 1
0.4%
1150 1
0.4%
1090 1
0.4%
1050 1
0.4%
990 1
0.4%
940 1
0.4%
930 1
0.4%
850 2
0.8%
820 2
0.8%
810 1
0.4%

Calories from Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.09615
Minimum0
Maximum1060
Zeros54
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:28.865820image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median100
Q3200
95-th percentile330
Maximum1060
Range1060
Interquartile range (IQR)180

Descriptive statistics

Standard deviation127.87591
Coefficient of variation (CV)1.0061352
Kurtosis10.605606
Mean127.09615
Median Absolute Deviation (MAD)90
Skewness2.1455081
Sum33045
Variance16352.249
MonotonicityNot monotonic
2024-04-26T23:23:29.528819image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 54
20.8%
80 16
 
6.2%
150 11
 
4.2%
120 10
 
3.8%
200 10
 
3.8%
210 9
 
3.5%
180 8
 
3.1%
280 8
 
3.1%
100 7
 
2.7%
40 7
 
2.7%
Other values (38) 120
46.2%
ValueCountFrequency (%)
0 54
20.8%
5 5
 
1.9%
10 5
 
1.9%
20 2
 
0.8%
30 6
 
2.3%
35 4
 
1.5%
40 7
 
2.7%
45 4
 
1.5%
50 4
 
1.5%
60 6
 
2.3%
ValueCountFrequency (%)
1060 1
0.4%
540 1
0.4%
530 1
0.4%
510 1
0.4%
470 1
0.4%
450 1
0.4%
430 1
0.4%
410 1
0.4%
380 1
0.4%
370 1
0.4%

Total Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.165385
Minimum0
Maximum118
Zeros49
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:30.891814image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.375
median11
Q322.25
95-th percentile37
Maximum118
Range118
Interquartile range (IQR)19.875

Descriptive statistics

Standard deviation14.205998
Coefficient of variation (CV)1.0028671
Kurtosis10.682321
Mean14.165385
Median Absolute Deviation (MAD)10
Skewness2.1527989
Sum3683
Variance201.81038
MonotonicityNot monotonic
2024-04-26T23:23:31.771817image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
18.8%
9 14
 
5.4%
23 12
 
4.6%
16 10
 
3.8%
3.5 8
 
3.1%
0.5 8
 
3.1%
20 8
 
3.1%
8 8
 
3.1%
14 7
 
2.7%
11 7
 
2.7%
Other values (42) 129
49.6%
ValueCountFrequency (%)
0 49
18.8%
0.5 8
 
3.1%
1 6
 
2.3%
1.5 1
 
0.4%
2 1
 
0.4%
2.5 1
 
0.4%
3.5 8
 
3.1%
4 3
 
1.2%
4.5 6
 
2.3%
5 5
 
1.9%
ValueCountFrequency (%)
118 1
0.4%
60 1
0.4%
59 1
0.4%
56 1
0.4%
52 1
0.4%
50 1
0.4%
48 1
0.4%
46 1
0.4%
43 1
0.4%
41 1
0.4%

Total Fat (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.815385
Minimum0
Maximum182
Zeros49
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:32.279816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median17
Q335
95-th percentile57
Maximum182
Range182
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation21.885199
Coefficient of variation (CV)1.0032002
Kurtosis10.756404
Mean21.815385
Median Absolute Deviation (MAD)16
Skewness2.1620114
Sum5672
Variance478.96192
MonotonicityNot monotonic
2024-04-26T23:23:33.216818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
18.8%
13 11
 
4.2%
1 11
 
4.2%
35 10
 
3.8%
25 9
 
3.5%
21 8
 
3.1%
14 7
 
2.7%
6 6
 
2.3%
8 6
 
2.3%
7 6
 
2.3%
Other values (53) 137
52.7%
ValueCountFrequency (%)
0 49
18.8%
1 11
 
4.2%
2 4
 
1.5%
3 1
 
0.4%
4 1
 
0.4%
5 5
 
1.9%
6 6
 
2.3%
7 6
 
2.3%
8 6
 
2.3%
9 2
 
0.8%
ValueCountFrequency (%)
182 1
0.4%
93 1
0.4%
91 1
0.4%
87 1
0.4%
80 1
0.4%
77 1
0.4%
73 1
0.4%
70 1
0.4%
66 1
0.4%
63 1
0.4%

Saturated Fat
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0076923
Minimum0
Maximum20
Zeros60
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:33.977814image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q310
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.3218732
Coefficient of variation (CV)0.88584317
Kurtosis-0.42646629
Mean6.0076923
Median Absolute Deviation (MAD)4.25
Skewness0.6636848
Sum1562
Variance28.322334
MonotonicityNot monotonic
2024-04-26T23:23:34.453498image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 60
23.1%
8 18
 
6.9%
10 16
 
6.2%
6 15
 
5.8%
3 15
 
5.8%
7 13
 
5.0%
12 12
 
4.6%
5 11
 
4.2%
4.5 11
 
4.2%
9 11
 
4.2%
Other values (16) 78
30.0%
ValueCountFrequency (%)
0 60
23.1%
0.5 3
 
1.2%
1 3
 
1.2%
1.5 5
 
1.9%
2 10
 
3.8%
2.5 6
 
2.3%
3 15
 
5.8%
3.5 9
 
3.5%
4 3
 
1.2%
4.5 11
 
4.2%
ValueCountFrequency (%)
20 4
 
1.5%
19 2
 
0.8%
18 1
 
0.4%
17 4
 
1.5%
16 3
 
1.2%
15 8
3.1%
14 7
2.7%
13 7
2.7%
12 12
4.6%
11 3
 
1.2%

Saturated Fat (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.965385
Minimum0
Maximum102
Zeros60
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:35.237435image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median24
Q348
95-th percentile78
Maximum102
Range102
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation26.639209
Coefficient of variation (CV)0.8889994
Kurtosis-0.36824864
Mean29.965385
Median Absolute Deviation (MAD)21
Skewness0.68529616
Sum7791
Variance709.64745
MonotonicityNot monotonic
2024-04-26T23:23:35.666431image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
23.1%
15 10
 
3.8%
22 10
 
3.8%
11 7
 
2.7%
29 7
 
2.7%
24 6
 
2.3%
40 6
 
2.3%
39 6
 
2.3%
30 5
 
1.9%
18 5
 
1.9%
Other values (64) 138
53.1%
ValueCountFrequency (%)
0 60
23.1%
3 3
 
1.2%
4 2
 
0.8%
5 1
 
0.4%
6 1
 
0.4%
8 4
 
1.5%
9 3
 
1.2%
10 2
 
0.8%
11 7
 
2.7%
12 2
 
0.8%
ValueCountFrequency (%)
102 1
0.4%
101 2
0.8%
100 1
0.4%
96 2
0.8%
90 1
0.4%
87 2
0.8%
85 2
0.8%
81 1
0.4%
78 2
0.8%
76 1
0.4%

Trans Fat
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0.0
204 
1.0
30 
0.5
 
17
1.5
 
8
2.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters780
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 204
78.5%
1.0 30
 
11.5%
0.5 17
 
6.5%
1.5 8
 
3.1%
2.5 1
 
0.4%

Length

2024-04-26T23:23:35.979439image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-26T23:23:36.261434image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 204
78.5%
1.0 30
 
11.5%
0.5 17
 
6.5%
1.5 8
 
3.1%
2.5 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 455
58.3%
. 260
33.3%
1 38
 
4.9%
5 26
 
3.3%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 455
58.3%
. 260
33.3%
1 38
 
4.9%
5 26
 
3.3%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 455
58.3%
. 260
33.3%
1 38
 
4.9%
5 26
 
3.3%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 455
58.3%
. 260
33.3%
1 38
 
4.9%
5 26
 
3.3%
2 1
 
0.1%

Cholesterol
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.942308
Minimum0
Maximum575
Zeros44
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:36.694436image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median35
Q365
95-th percentile250
Maximum575
Range575
Interquartile range (IQR)60

Descriptive statistics

Standard deviation87.269257
Coefficient of variation (CV)1.5883799
Kurtosis17.388874
Mean54.942308
Median Absolute Deviation (MAD)30
Skewness3.7989073
Sum14285
Variance7615.9233
MonotonicityNot monotonic
2024-04-26T23:23:37.439432image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 44
16.9%
5 24
 
9.2%
35 21
 
8.1%
25 17
 
6.5%
40 14
 
5.4%
30 14
 
5.4%
50 12
 
4.6%
15 12
 
4.6%
10 9
 
3.5%
65 8
 
3.1%
Other values (25) 85
32.7%
ValueCountFrequency (%)
0 44
16.9%
5 24
9.2%
10 9
 
3.5%
15 12
 
4.6%
20 8
 
3.1%
25 17
 
6.5%
30 14
 
5.4%
35 21
8.1%
40 14
 
5.4%
45 7
 
2.7%
ValueCountFrequency (%)
575 2
 
0.8%
555 2
 
0.8%
300 1
 
0.4%
295 1
 
0.4%
285 1
 
0.4%
280 1
 
0.4%
275 1
 
0.4%
265 2
 
0.8%
260 1
 
0.4%
250 5
1.9%

Cholesterol (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.392308
Minimum0
Maximum192
Zeros44
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:37.834473image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q321.25
95-th percentile83.05
Maximum192
Range192
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation29.091653
Coefficient of variation (CV)1.5817293
Kurtosis17.433757
Mean18.392308
Median Absolute Deviation (MAD)9
Skewness3.8042
Sum4782
Variance846.32426
MonotonicityNot monotonic
2024-04-26T23:23:38.204431image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 44
 
16.9%
2 18
 
6.9%
11 16
 
6.2%
9 13
 
5.0%
14 13
 
5.0%
12 11
 
4.2%
17 9
 
3.5%
3 9
 
3.5%
6 8
 
3.1%
10 7
 
2.7%
Other values (38) 112
43.1%
ValueCountFrequency (%)
0 44
16.9%
1 6
 
2.3%
2 18
6.9%
3 9
 
3.5%
5 6
 
2.3%
6 8
 
3.1%
7 6
 
2.3%
8 5
 
1.9%
9 13
 
5.0%
10 7
 
2.7%
ValueCountFrequency (%)
192 2
0.8%
185 2
0.8%
100 1
0.4%
99 1
0.4%
95 1
0.4%
93 1
0.4%
92 1
0.4%
89 2
0.8%
87 1
0.4%
84 1
0.4%

Sodium
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean495.75
Minimum0
Maximum3600
Zeros9
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:38.542522image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1107.5
median190
Q3865
95-th percentile1511.5
Maximum3600
Range3600
Interquartile range (IQR)757.5

Descriptive statistics

Standard deviation577.02632
Coefficient of variation (CV)1.1639462
Kurtosis2.8743805
Mean495.75
Median Absolute Deviation (MAD)147.5
Skewness1.5440882
Sum128895
Variance332959.38
MonotonicityNot monotonic
2024-04-26T23:23:39.143495image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 11
 
4.2%
0 9
 
3.5%
150 8
 
3.1%
190 8
 
3.1%
140 8
 
3.1%
10 7
 
2.7%
135 7
 
2.7%
170 6
 
2.3%
50 6
 
2.3%
115 6
 
2.3%
Other values (101) 184
70.8%
ValueCountFrequency (%)
0 9
3.5%
5 5
1.9%
10 7
2.7%
15 3
 
1.2%
20 2
 
0.8%
25 1
 
0.4%
30 2
 
0.8%
35 4
1.5%
40 3
 
1.2%
45 3
 
1.2%
ValueCountFrequency (%)
3600 1
0.4%
2290 1
0.4%
2260 1
0.4%
2170 1
0.4%
2150 1
0.4%
1800 1
0.4%
1720 1
0.4%
1700 1
0.4%
1680 1
0.4%
1590 1
0.4%

Sodium (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.676923
Minimum0
Maximum150
Zeros20
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:39.552509image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median8
Q336.25
95-th percentile63.05
Maximum150
Range150
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation24.034954
Coefficient of variation (CV)1.1624048
Kurtosis2.8838631
Mean20.676923
Median Absolute Deviation (MAD)6
Skewness1.5459861
Sum5376
Variance577.679
MonotonicityNot monotonic
2024-04-26T23:23:40.104504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 23
 
8.8%
0 20
 
7.7%
7 17
 
6.5%
5 16
 
6.2%
8 16
 
6.2%
2 15
 
5.8%
3 13
 
5.0%
1 12
 
4.6%
9 9
 
3.5%
10 7
 
2.7%
Other values (55) 112
43.1%
ValueCountFrequency (%)
0 20
7.7%
1 12
4.6%
2 15
5.8%
3 13
5.0%
4 5
 
1.9%
5 16
6.2%
6 23
8.8%
7 17
6.5%
8 16
6.2%
9 9
 
3.5%
ValueCountFrequency (%)
150 1
0.4%
95 1
0.4%
94 1
0.4%
91 1
0.4%
90 1
0.4%
75 1
0.4%
72 1
0.4%
71 1
0.4%
70 1
0.4%
66 1
0.4%

Carbohydrates
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.346154
Minimum0
Maximum141
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:40.588498image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median44
Q360
95-th percentile110.05
Maximum141
Range141
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.252232
Coefficient of variation (CV)0.59671651
Kurtosis1.4074474
Mean47.346154
Median Absolute Deviation (MAD)14
Skewness0.91269932
Sum12310
Variance798.1886
MonotonicityNot monotonic
2024-04-26T23:23:41.070502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
6.2%
50 9
 
3.5%
30 8
 
3.1%
42 7
 
2.7%
41 7
 
2.7%
47 6
 
2.3%
55 6
 
2.3%
38 6
 
2.3%
43 6
 
2.3%
60 6
 
2.3%
Other values (74) 183
70.4%
ValueCountFrequency (%)
0 16
6.2%
4 2
 
0.8%
7 1
 
0.4%
8 1
 
0.4%
9 1
 
0.4%
10 1
 
0.4%
12 3
 
1.2%
15 4
 
1.5%
18 3
 
1.2%
19 1
 
0.4%
ValueCountFrequency (%)
141 1
 
0.4%
140 1
 
0.4%
139 1
 
0.4%
135 2
0.8%
118 1
 
0.4%
116 1
 
0.4%
115 1
 
0.4%
114 3
1.2%
111 2
0.8%
110 1
 
0.4%

Carbohydrates (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.780769
Minimum0
Maximum47
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:41.577502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q320
95-th percentile37
Maximum47
Range47
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.4195443
Coefficient of variation (CV)0.5969002
Kurtosis1.3952351
Mean15.780769
Median Absolute Deviation (MAD)5
Skewness0.90359945
Sum4103
Variance88.727814
MonotonicityNot monotonic
2024-04-26T23:23:41.898136image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
14 20
 
7.7%
0 16
 
6.2%
13 16
 
6.2%
16 15
 
5.8%
10 15
 
5.8%
17 14
 
5.4%
15 13
 
5.0%
20 12
 
4.6%
19 11
 
4.2%
18 11
 
4.2%
Other values (30) 117
45.0%
ValueCountFrequency (%)
0 16
6.2%
1 2
 
0.8%
2 1
 
0.4%
3 3
 
1.2%
4 3
 
1.2%
5 4
 
1.5%
6 4
 
1.5%
7 8
3.1%
8 7
2.7%
9 8
3.1%
ValueCountFrequency (%)
47 2
0.8%
46 1
 
0.4%
45 2
0.8%
39 2
0.8%
38 4
1.5%
37 3
1.2%
36 2
0.8%
35 1
 
0.4%
33 1
 
0.4%
32 2
0.8%

Dietary Fiber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6307692
Minimum0
Maximum7
Zeros69
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:42.160134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5677171
Coefficient of variation (CV)0.96133595
Kurtosis1.3236325
Mean1.6307692
Median Absolute Deviation (MAD)1
Skewness1.1736248
Sum424
Variance2.4577369
MonotonicityNot monotonic
2024-04-26T23:23:42.419134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 80
30.8%
0 69
26.5%
2 44
16.9%
3 38
14.6%
4 15
 
5.8%
5 6
 
2.3%
6 4
 
1.5%
7 4
 
1.5%
ValueCountFrequency (%)
0 69
26.5%
1 80
30.8%
2 44
16.9%
3 38
14.6%
4 15
 
5.8%
5 6
 
2.3%
6 4
 
1.5%
7 4
 
1.5%
ValueCountFrequency (%)
7 4
 
1.5%
6 4
 
1.5%
5 6
 
2.3%
4 15
 
5.8%
3 38
14.6%
2 44
16.9%
1 80
30.8%
0 69
26.5%

Dietary Fiber (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5307692
Minimum0
Maximum28
Zeros69
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:42.674132image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile19
Maximum28
Range28
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.3070573
Coefficient of variation (CV)0.96574494
Kurtosis1.2066887
Mean6.5307692
Median Absolute Deviation (MAD)5
Skewness1.1542451
Sum1698
Variance39.778972
MonotonicityNot monotonic
2024-04-26T23:23:42.943130image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 69
26.5%
6 23
 
8.8%
4 22
 
8.5%
5 21
 
8.1%
3 19
 
7.3%
7 14
 
5.4%
13 12
 
4.6%
12 10
 
3.8%
8 10
 
3.8%
10 9
 
3.5%
Other values (14) 51
19.6%
ValueCountFrequency (%)
0 69
26.5%
2 5
 
1.9%
3 19
 
7.3%
4 22
 
8.5%
5 21
 
8.1%
6 23
 
8.8%
7 14
 
5.4%
8 10
 
3.8%
9 5
 
1.9%
10 9
 
3.5%
ValueCountFrequency (%)
28 4
1.5%
24 1
 
0.4%
23 3
1.2%
22 2
 
0.8%
20 1
 
0.4%
19 3
1.2%
18 1
 
0.4%
17 7
2.7%
16 1
 
0.4%
15 4
1.5%

Sugars
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.423077
Minimum0
Maximum128
Zeros25
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:43.412757image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median17.5
Q348
95-th percentile85.15
Maximum128
Range128
Interquartile range (IQR)42.25

Descriptive statistics

Standard deviation28.679797
Coefficient of variation (CV)0.97473819
Kurtosis0.52069225
Mean29.423077
Median Absolute Deviation (MAD)16.5
Skewness1.0319403
Sum7650
Variance822.53074
MonotonicityNot monotonic
2024-04-26T23:23:43.764124image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
9.6%
3 17
 
6.5%
2 11
 
4.2%
7 10
 
3.8%
6 8
 
3.1%
12 7
 
2.7%
15 7
 
2.7%
59 6
 
2.3%
4 6
 
2.3%
10 6
 
2.3%
Other values (73) 157
60.4%
ValueCountFrequency (%)
0 25
9.6%
1 2
 
0.8%
2 11
4.2%
3 17
6.5%
4 6
 
2.3%
5 4
 
1.5%
6 8
 
3.1%
7 10
 
3.8%
8 4
 
1.5%
9 4
 
1.5%
ValueCountFrequency (%)
128 1
0.4%
123 1
0.4%
120 1
0.4%
115 1
0.4%
103 1
0.4%
101 1
0.4%
100 1
0.4%
99 1
0.4%
97 1
0.4%
93 1
0.4%

Protein
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.338462
Minimum0
Maximum87
Zeros27
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:44.086618image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median12
Q319
95-th percentile36
Maximum87
Range87
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.426146
Coefficient of variation (CV)0.85663149
Kurtosis6.0013288
Mean13.338462
Median Absolute Deviation (MAD)8
Skewness1.5799238
Sum3468
Variance130.55682
MonotonicityNot monotonic
2024-04-26T23:23:44.392618image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 27
 
10.4%
1 18
 
6.9%
9 14
 
5.4%
12 14
 
5.4%
2 13
 
5.0%
15 12
 
4.6%
14 12
 
4.6%
11 12
 
4.6%
10 11
 
4.2%
16 10
 
3.8%
Other values (32) 117
45.0%
ValueCountFrequency (%)
0 27
10.4%
1 18
6.9%
2 13
5.0%
3 5
 
1.9%
4 7
 
2.7%
5 3
 
1.2%
6 4
 
1.5%
7 3
 
1.2%
8 9
 
3.5%
9 14
5.4%
ValueCountFrequency (%)
87 1
 
0.4%
48 1
 
0.4%
44 1
 
0.4%
40 2
 
0.8%
39 1
 
0.4%
37 2
 
0.8%
36 6
2.3%
35 2
 
0.8%
33 1
 
0.4%
32 2
 
0.8%

Vitamin A (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.426923
Minimum0
Maximum170
Zeros62
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:44.653617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q315
95-th percentile60
Maximum170
Range170
Interquartile range (IQR)13

Descriptive statistics

Standard deviation24.366381
Coefficient of variation (CV)1.8147405
Kurtosis24.510011
Mean13.426923
Median Absolute Deviation (MAD)7
Skewness4.5803307
Sum3491
Variance593.72051
MonotonicityNot monotonic
2024-04-26T23:23:44.930622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 62
23.8%
10 40
15.4%
15 40
15.4%
4 25
9.6%
2 18
 
6.9%
20 17
 
6.5%
8 15
 
5.8%
6 15
 
5.8%
60 7
 
2.7%
25 6
 
2.3%
Other values (9) 15
 
5.8%
ValueCountFrequency (%)
0 62
23.8%
2 18
 
6.9%
4 25
9.6%
6 15
 
5.8%
8 15
 
5.8%
10 40
15.4%
15 40
15.4%
20 17
 
6.5%
25 6
 
2.3%
30 5
 
1.9%
ValueCountFrequency (%)
170 3
1.2%
160 1
 
0.4%
110 1
 
0.4%
100 1
 
0.4%
70 1
 
0.4%
60 7
2.7%
50 1
 
0.4%
45 1
 
0.4%
40 1
 
0.4%
30 5
1.9%

Vitamin C (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5346154
Minimum0
Maximum240
Zeros162
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:45.255616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile30
Maximum240
Range240
Interquartile range (IQR)4

Descriptive statistics

Standard deviation26.345542
Coefficient of variation (CV)3.0869045
Kurtosis35.250745
Mean8.5346154
Median Absolute Deviation (MAD)0
Skewness5.5022308
Sum2219
Variance694.0876
MonotonicityNot monotonic
2024-04-26T23:23:45.512658image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 162
62.3%
2 31
 
11.9%
15 12
 
4.6%
8 9
 
3.5%
30 8
 
3.1%
25 8
 
3.1%
20 7
 
2.7%
4 6
 
2.3%
10 5
 
1.9%
130 3
 
1.2%
Other values (7) 9
 
3.5%
ValueCountFrequency (%)
0 162
62.3%
2 31
 
11.9%
4 6
 
2.3%
6 1
 
0.4%
8 9
 
3.5%
10 5
 
1.9%
15 12
 
4.6%
20 7
 
2.7%
25 8
 
3.1%
30 8
 
3.1%
ValueCountFrequency (%)
240 1
 
0.4%
160 2
 
0.8%
130 3
 
1.2%
100 1
 
0.4%
70 1
 
0.4%
45 2
 
0.8%
35 1
 
0.4%
30 8
3.1%
25 8
3.1%
20 7
2.7%

Calcium (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.973077
Minimum0
Maximum70
Zeros36
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:45.778623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median20
Q330
95-th percentile50
Maximum70
Range70
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.019953
Coefficient of variation (CV)0.81151437
Kurtosis-0.47598883
Mean20.973077
Median Absolute Deviation (MAD)12
Skewness0.59355457
Sum5453
Variance289.67881
MonotonicityNot monotonic
2024-04-26T23:23:46.122618image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 36
13.8%
30 33
12.7%
15 25
9.6%
25 22
8.5%
20 20
7.7%
50 20
7.7%
10 18
6.9%
8 17
6.5%
40 17
6.5%
4 14
 
5.4%
Other values (6) 38
14.6%
ValueCountFrequency (%)
0 36
13.8%
2 10
 
3.8%
4 14
 
5.4%
6 6
 
2.3%
8 17
6.5%
10 18
6.9%
15 25
9.6%
20 20
7.7%
25 22
8.5%
30 33
12.7%
ValueCountFrequency (%)
70 2
 
0.8%
60 6
 
2.3%
50 20
7.7%
45 4
 
1.5%
40 17
6.5%
35 10
 
3.8%
30 33
12.7%
25 22
8.5%
20 20
7.7%
15 25
9.6%

Iron (% Daily Value)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7346154
Minimum0
Maximum40
Zeros82
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-26T23:23:46.406620image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q315
95-th percentile25
Maximum40
Range40
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7232633
Coefficient of variation (CV)1.1278212
Kurtosis0.92479614
Mean7.7346154
Median Absolute Deviation (MAD)4
Skewness1.1879081
Sum2011
Variance76.095322
MonotonicityNot monotonic
2024-04-26T23:23:46.762659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 82
31.5%
2 33
12.7%
15 31
 
11.9%
20 25
 
9.6%
10 20
 
7.7%
4 20
 
7.7%
6 17
 
6.5%
8 16
 
6.2%
25 7
 
2.7%
30 6
 
2.3%
Other values (2) 3
 
1.2%
ValueCountFrequency (%)
0 82
31.5%
2 33
12.7%
4 20
 
7.7%
6 17
 
6.5%
8 16
 
6.2%
10 20
 
7.7%
15 31
 
11.9%
20 25
 
9.6%
25 7
 
2.7%
30 6
 
2.3%
ValueCountFrequency (%)
40 2
 
0.8%
35 1
 
0.4%
30 6
 
2.3%
25 7
 
2.7%
20 25
9.6%
15 31
11.9%
10 20
7.7%
8 16
6.2%
6 17
6.5%
4 20
7.7%

Interactions

2024-04-26T23:23:09.272144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:18.210147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:23.399238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:27.934860image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:33.256998image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:38.997823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:44.093454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:49.200425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:53.847964image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:00.793920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:06.118332image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:11.059517image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:15.653306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:22.539247image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:28.417241image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:36.256274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:42.333097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:46.804762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:53.847761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:00.971149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:10.022148image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:18.447152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:23.733242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:28.180864image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:33.698101image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:39.241791image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:44.508453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:49.445427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:54.076961image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:01.237919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:06.343328image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:11.286520image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:16.025273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:22.954235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:28.728235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:36.498236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:42.540054image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:47.121758image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:54.090795image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:01.502147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:10.495150image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:18.661150image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:23.923244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:28.391862image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:33.935032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:39.469825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:44.730453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:49.649463image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:54.292957image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:01.539917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:06.553363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:11.499520image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:16.221235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:23.280234image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:29.175235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:36.698276image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:42.733793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:47.491756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:54.313794image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:02.325148image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:11.260152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:18.872186image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:24.142279image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:28.596869image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:34.179576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:39.699825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:44.971448image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:49.861428image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:54.525917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:01.774920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:06.769363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:11.714311image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:16.426275image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:23.538233image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:29.413236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:36.903242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:42.932795image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:47.721761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:54.586754image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:02.666144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:11.795146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:19.111250image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:24.360899image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:28.827900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:34.407476image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:39.939789image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:45.277453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:50.129431image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:54.751954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:02.078923image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:06.999371image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:11.992336image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:16.635273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:23.816240image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:29.647274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:37.329237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:43.148762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:47.946795image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:54.906756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:03.210146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:12.660148image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:19.348251image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:24.592863image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:29.058897image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:34.665823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:40.191825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:45.521490image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:50.393432image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:55.047919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:02.322955image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:07.408347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:12.232304image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:16.864277image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:24.208237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:30.323238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:37.630273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:43.370794image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:48.197756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:55.323760image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:03.607144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:13.033147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:19.653244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:24.801861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:29.276901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:34.974786image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:40.410785image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-04-26T23:21:58.766917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:04.831331image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:09.940518image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:14.746298image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:21.417238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:27.446243image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:34.857248image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:41.173237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:45.816763image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:52.772762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:59.658144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:07.950147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:17.539821image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:22.493244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:27.273899image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:32.363997image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:38.291784image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:43.367458image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:48.528670image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:53.021956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:59.068918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:05.110335image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:10.347519image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:14.975301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:21.729268image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:27.688239image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:35.312236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:41.555235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:46.032793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:53.019790image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:59.885185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:08.326148image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:18.741814image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:22.720239image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:27.477900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:32.604995image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:38.510823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:43.582450image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:48.737426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:53.226920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:59.574920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:05.497367image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:10.562481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:15.195299image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:21.943273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:27.914242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:35.713238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:41.790235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:46.255759image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:53.279756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:00.216148image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:08.661151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:19.203815image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:23.080242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:27.694863image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:32.882998image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:38.753825image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:43.842488image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:48.962426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:21:53.457920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:00.120921image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:05.878366image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:10.801485image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:15.419298image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:22.290238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:28.181238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:36.019236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:42.021057image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:46.532757image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:22:53.550792image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:00.480145image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-04-26T23:23:08.966149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-04-26T23:23:47.136625image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Calcium (% Daily Value)CaloriesCalories from FatCarbohydratesCarbohydrates (% Daily Value)CategoryCholesterolCholesterol (% Daily Value)Dietary FiberDietary Fiber (% Daily Value)Iron (% Daily Value)ProteinSaturated FatSaturated Fat (% Daily Value)SodiumSodium (% Daily Value)SugarsTotal FatTotal Fat (% Daily Value)Trans FatVitamin A (% Daily Value)Vitamin C (% Daily Value)
Calcium (% Daily Value)1.0000.5140.3140.5270.5270.3430.4820.4850.1890.2080.1840.5270.4570.4570.3540.3550.5480.3280.3280.2950.759-0.230
Calories0.5141.0000.8930.7320.7310.2760.8370.8290.5630.5740.6880.7850.8620.8650.7910.7910.2290.8960.8960.3430.4540.207
Calories from Fat0.3140.8931.0000.4020.4010.2780.8780.8730.6010.5980.7470.7570.9240.9270.8410.840-0.1120.9980.9980.3590.3020.272
Carbohydrates0.5270.7320.4021.0000.9990.2460.3880.3800.2430.2580.2510.3530.4670.4650.3030.3030.7220.4080.4070.3310.434-0.003
Carbohydrates (% Daily Value)0.5270.7310.4010.9991.0000.2460.3870.3790.2400.2540.2490.3510.4670.4660.3010.3010.7230.4070.4060.3360.434-0.005
Category0.3430.2760.2780.2460.2461.000-0.110-0.110-0.169-0.146-0.294-0.190-0.059-0.063-0.264-0.2670.376-0.142-0.1430.3360.331-0.148
Cholesterol0.4820.8370.8780.3880.387-0.1101.0000.9990.4930.5080.6910.8170.8670.8690.7920.7920.0090.8780.8800.5020.5100.207
Cholesterol (% Daily Value)0.4850.8290.8730.3800.379-0.1100.9991.0000.4850.5010.6800.8130.8660.8680.7830.7840.0090.8730.8750.5020.5120.201
Dietary Fiber0.1890.5630.6010.2430.240-0.1690.4930.4851.0000.9790.8380.7020.4030.4100.7490.747-0.2480.6050.6040.1220.1840.568
Dietary Fiber (% Daily Value)0.2080.5740.5980.2580.254-0.1460.5080.5010.9791.0000.8430.7270.4020.4080.7560.752-0.2370.6040.6040.1320.2160.578
Iron (% Daily Value)0.1840.6880.7470.2510.249-0.2940.6910.6800.8380.8431.0000.7970.5800.5860.8710.867-0.2790.7490.7510.5870.2160.504
Protein0.5270.7850.7570.3530.351-0.1900.8170.8130.7020.7270.7971.0000.6440.6500.9210.922-0.1020.7650.7670.4480.4630.366
Saturated Fat0.4570.8620.9240.4670.467-0.0590.8670.8660.4030.4020.5800.6441.0000.9990.6950.6940.1020.9230.9240.4230.4130.051
Saturated Fat (% Daily Value)0.4570.8650.9270.4650.466-0.0630.8690.8680.4100.4080.5860.6500.9991.0000.7010.7010.0970.9260.9270.4190.4130.054
Sodium0.3540.7910.8410.3030.301-0.2640.7920.7830.7490.7560.8710.9210.6950.7011.0000.999-0.2480.8470.8480.1680.3160.374
Sodium (% Daily Value)0.3550.7910.8400.3030.301-0.2670.7920.7840.7470.7520.8670.9220.6940.7010.9991.000-0.2470.8460.8470.1700.3170.374
Sugars0.5480.229-0.1120.7220.7230.3760.0090.009-0.248-0.237-0.279-0.1020.1020.097-0.248-0.2471.000-0.105-0.1060.3190.434-0.348
Total Fat0.3280.8960.9980.4080.407-0.1420.8780.8730.6050.6040.7490.7650.9230.9260.8470.846-0.1051.0001.0000.3540.3100.264
Total Fat (% Daily Value)0.3280.8960.9980.4070.406-0.1430.8800.8750.6040.6040.7510.7670.9240.9270.8480.847-0.1061.0001.0000.3600.3110.265
Trans Fat0.2950.3430.3590.3310.3360.3360.5020.5020.1220.1320.5870.4480.4230.4190.1680.1700.3190.3540.3601.0000.4190.036
Vitamin A (% Daily Value)0.7590.4540.3020.4340.4340.3310.5100.5120.1840.2160.2160.4630.4130.4130.3160.3170.4340.3100.3110.4191.000-0.049
Vitamin C (% Daily Value)-0.2300.2070.272-0.003-0.005-0.1480.2070.2010.5680.5780.5040.3660.0510.0540.3740.374-0.3480.2640.2650.036-0.0491.000

Missing values

2024-04-26T23:23:19.809816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-26T23:23:21.614829image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CategoryItemServing SizeCaloriesCalories from FatTotal FatTotal Fat (% Daily Value)Saturated FatSaturated Fat (% Daily Value)Trans FatCholesterolCholesterol (% Daily Value)SodiumSodium (% Daily Value)CarbohydratesCarbohydrates (% Daily Value)Dietary FiberDietary Fiber (% Daily Value)SugarsProteinVitamin A (% Daily Value)Vitamin C (% Daily Value)Calcium (% Daily Value)Iron (% Daily Value)
0BreakfastEgg McMuffin4.8 oz (136 g)30012013.0205.0250.0260877503131104173171002515
1BreakfastEgg White Delight4.8 oz (135 g)250708.0123.0150.025877032301041731860258
2BreakfastSausage McMuffin3.9 oz (111 g)37020023.0358.0420.04515780332910417214802510
3BreakfastSausage McMuffin with Egg5.7 oz (161 g)45025028.04310.0520.0285958603630104172211503015
4BreakfastSausage McMuffin with Egg Whites5.7 oz (161 g)40021023.0358.0420.05016880373010417221602510
5BreakfastSteak & Egg McMuffin6.5 oz (185 g)43021023.0369.0461.03001009604031104183261523020
6BreakfastBacon, Egg & Cheese Biscuit (Regular Biscuit)5.3 oz (150 g)46023026.04013.0650.0250831300543813273191081515
7BreakfastBacon, Egg & Cheese Biscuit (Large Biscuit)5.8 oz (164 g)52027030.04714.0680.02508314105943143124191582020
8BreakfastBacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)5.4 oz (153 g)41018020.03211.0560.03511130054361227320281510
9BreakfastBacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)5.9 oz (167 g)47022025.03812.0590.035111420594214312420681515
CategoryItemServing SizeCaloriesCalories from FatTotal FatTotal Fat (% Daily Value)Saturated FatSaturated Fat (% Daily Value)Trans FatCholesterolCholesterol (% Daily Value)SodiumSodium (% Daily Value)CarbohydratesCarbohydrates (% Daily Value)Dietary FiberDietary Fiber (% Daily Value)SugarsProteinVitamin A (% Daily Value)Vitamin C (% Daily Value)Calcium (% Daily Value)Iron (% Daily Value)
250Smoothies & ShakesShamrock Shake (Medium)16 fl oz cup66017019.02912.0611.07524210910936009314250500
251Smoothies & ShakesShamrock Shake (Large)22 fl oz cup82021023.03515.0731.0902926011135450011518300600
252Smoothies & ShakesMcFlurry with M&M’s Candies (Small)10.9 oz (310 g)65021023.03514.0720.5501718079632168913150458
253Smoothies & ShakesMcFlurry with M&M’s Candies (Medium)16.2 oz (460 g)93029033.05020.01021.07525260111394627128202507010
254Smoothies & ShakesMcFlurry with M&M’s Candies (Snack)7.3 oz (207 g)43014015.02410.0480.035111205642114599100304
255Smoothies & ShakesMcFlurry with Oreo Cookies (Small)10.1 oz (285 g)51015017.0269.0440.54514280128027146412150408
256Smoothies & ShakesMcFlurry with Oreo Cookies (Medium)13.4 oz (381 g)69020023.03512.0581.0551938016106351585152005010
257Smoothies & ShakesMcFlurry with Oreo Cookies (Snack)6.7 oz (190 g)34010011.0176.0290.03091908531812438100256
258Smoothies & ShakesMcFlurry with Reese's Peanut Butter Cups (Medium)14.2 oz (403 g)81029032.05015.0761.0602040017114382910321200606
259Smoothies & ShakesMcFlurry with Reese's Peanut Butter Cups (Snack)7.1 oz (202 g)41015016.0258.0380.0301020085719155110100304